Telemedicine Visits in US Skilled Nursing Facilities

This cohort study examines Medicare data for residents in skilled nursing facilities to determine patterns in the use of telemedicine during the COVID-19 pandemic.

The full list of all CMS Provider Specialty codes is accessible at https://resdac.org/sites/datadocumentation.resdac.org/files/CMS_PRVDR_SPCLTY_TB_rev0 1242018_0.txt eTable 2. List of place-of-service, procedure, and modifier codes to identify telemedicine visits Category Codes Place of service 01, 02 Healthcare Common Procedure Coding System (HCPCS) G2025, G0406-8, G2010, G2012, G2061-3, G2250-2, 99421-3, 99441-3, 98970-2, 98966-8 HCPCS modifier codes GT, GQ, 95, G0, FQ, 93 Any of the codes would suffice to flag a visit as telemedicine. For SNF visits that were selected based on Healthcare Common Procedure Coding System (HCPCS) codes, only place of service and HCPCS modifier codes could be used to identify telemedicine visits. SNF -skilled nursing facility. CI -confidence interval. a -Area type is classified based on Rural-Urban Commuting Area Codes: metropolitan (1-3), micropolitan (4-6), small urban (7-9), or rural area (10). b -Medicare star ratings range from 1 (much below average) to 5 (much above average). This score is a composite ranking of individual SNFs that incorporates multiple measures of SNF quality, staffing, and health inspection performance. c -The proportion of Medicaid covered residents among all Medicare residents within the SNF. d -The mean proportion of residents of specific race/ethnicity represent the proportion of unique Medicare beneficiaries residing in the SNFs of that group. Odds ratios are provided for the variable scaled by 10 (the odds of SNF being in a high telemedicine use group associated with 10 percentage points increase in the race/ethnicity among all Medicare residents). Other race/ethnicity includes Asian, Hispanic, North American native, other, and missing. Included are SNFs that had at least 10 SNF visits for their residents in the analyzed year. Ownership, chain, beds, and rating information is from January 2020 (CMS Nursing Homes Compare). SNFs with missing information in CMS Nursing Homes Compare are excluded (N=832 in 2020; N=1789 in 2021). eTable 6. Characteristics of SNF-visit-providing clinicians in the top decile by telemedicine use in 2020-2021 SNF -skilled nursing facility. Clinicians in the top decile by telemedicine use had more than 18.6% of their SNF visits delivered in telemedicine. Only clinicians with at least 20 total SNF visits in 2020-2021 combined are included. Gender was missing for 9,690 (18.5%), year of graduation for 9,720 (18.6%) and geography (ZIP area information) for 9,710 (18.6%) clinicians. eFigure 3. Proportion of visits delivered by telemedicine in four examined clinical care scenarios for long-term care residents in high-and low-telemedicine use SNFs in 2018-2021 SNF -skilled nursing facility. High-use SNFs were defined as those in the top quartile by telemedicine use in 2020 for SNF or outpatient visits (depending on the analyzed outcome), and low-use as those in the bottom quartile. To examine if the outcomes studied in the difference-in-differences analysis were evolving in parallel in 2018-2019 (before the expansion of telemedicine) in the groups of high and low telemedicine adopting SNFs, we implemented linear regression models:

Long-term residents
is a linear variable for the year of the outcome; _ is a binary indicator of whether the patient stayed at a SNF with high telemedicine use in 2020; * _ is the interaction term capturing if the outcome trends in 2017-2019 in high and low telemedicine SNFs are not parallel; is a fixed effect for the state where SNF is located; is the set of resident characteristics (indicators for sex, race, dual Medicaid eligibility, original reason for Medicare enrolment, presence of dementia, and continuous variables for the number of chronic conditions and age); is the error with SNF level clustering.
As in the main models, we weighted observations by the length of stay at SNF in days  The estimated interaction term is not statistically significant for new outpatient consultations with specialist physicians, which can be interpreted as linear (parallel) trends for these outcomes in 2018-2019.
It is statistically significant for outpatient visits for residents with limited mobility, SNF visits on weekends, and psychiatrist visits: in 2018-2019, high telemedicine using SNFs were increasing the number of visits slower than low telemedicine using SNFs. The differential pre-trends could mean that these groups of SNFs were potentially different in relevant ways in respect to these outcomes even before the expansion of telemedicine. eAppendix 3. Difference-in-differences in visit counts in 2018-2019 vs 2020-2021 in high vs low telemedicine adopting SNFs The results, presented in the main manuscript, were built with linear regression models, in which observations were weighted by the length of stay at SNF in days (Model 1): is a fixed effect for the year of the outcome; _ is a binary indicator of whether the patient stayed at a SNF with high telemedicine use in 2020; * _ is the interaction term equal to 1 in high telemedicine use SNFs in 2020-2021 and equal to 0 in low telemedicine use SNFs and earlier years; is a fixed effect for the state where SNF is located; is the set of resident characteristics (indicators for sex, race, dual Medicaid eligibility, original reason for Medicare enrolment, presence of dementia, and continuous variables for the number of chronic conditions and age); is the error with SNF level clustering.
The number of observations (resident-year combinations) for each model are provided below. Note that one observation reflects one resident-SNF-year pair (representing at least 60 days of SNF stay).  Table 2 in the main manuscript presents estimates of the interaction term, corresponding to difference-in-differences estimate.
eAppendix 4. Difference-in-differences models with a separate term for 2020 and 2021 To explore if high-telemedicine use in 2020 was associated with different outcomes in year 2020 and year 2021, we implemented a model with separate terms for these years: is a fixed effect for the year of the outcome; _ is a binary indicator of whether the patient stayed at a SNF with high telemedicine use in 2020; * _ and * _ are the interaction terms equal to 1 in high telemedicine use SNFs in 2020 and in 2021 respectively, and equal to 0 in low telemedicine use SNFs and other years; is a fixed effect for the state where SNF is located; is the set of resident characteristics (indicators for sex, race, dual Medicaid eligibility, original reason for Medicare enrolment, presence of dementia, and continuous variables for the number of chronic conditions and age); is the error with SNF level clustering.
The estimated interaction terms are provided in the